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Grand Challenges is a family of initiatives fostering innovation to solve key global health and development problems. Each initiative is an experiment in the use of challenges to focus innovation on making an impact. Individual challenges address some of the same problems, but from differing perspectives.

Agnes FigueiredoUniversidade Federal do Rio de JaneiroRio de Janeiro, Rio de Janeiro, Brazil

Grand Challenges Brazil

Drug Resistance Burden

1 Nov 2018

The project will use molecular approaches, including genomics and phylogenomics, to find biomarkers that could indicate the location in the genetic code driving bacterial adaptation. In addition, these biomarkers could be used as a rapid method for screening predominant and high-virulency MRSA clones in hospitals, and thus quickly provide infection control committees with key data on MRSA spread and its antimicrobial resistance profile.

The idea is to develop an artificial intelligence model capable of simultaneously analyzing data from the Laboratory Information System and from the Hospital Information System. This technology aims to enable the delivery to hospital physicians of a ranked list of antimicrobials that are more suitable to treat infection by multi-resistant microorganism with a focus on newborn and young children.

The project will study the genetic material from environmental samples from humans (healthy and ill), cattle and their meat to estimate the proportion of E. coli and K. pneumoniae in the microbiome. The main objective is to better understand the distribution of bacteria and its resistance genes, Escherichia coli and Klebsiella pneumoniae bacteria and extended spectrum beta-lactamase (EsβL) and carbapenemases encoding genes in distinct ecological sources.

This project proposes the development of the One Health Brazilian Resistance (OneBR), a curated and integrated genomic database. OneBR will use algorithms based on artificial intelligence to conduct surveillance, diagnosis, management and treatment of antimicrobial resistance (AMR) in the human-animal-environment interface. The goal is for this platform to be used by Brazilian health professionals in diverse settings, particularly within the Unified Healthcare System (SUS).

Rejane PinheiroUniversidade Federal do Rio de JaneiroRio de Janeiro, Rio de Janeiro, Brazil

Grand Challenges Brazil

Drug Resistance Burden

1 Nov 2018

The researcher will use machine learning techniques and a linked database to analyze mortality from drug-resistant tuberculosis. The goal is to better understand how the flow of patients through the health services network have influenced, or not, the occurrence of resistance.

The project proposes to characterize the resistant determinants of microbial communities from key sources in hospitals, environment and farms to model the dynamics of the flow of antibiotic resistant microorganisms. The goal is to understand how the hospital environment and animal farming affect the ecology of antibiotic resistance movement. The project will rely on a methodology that allows the analysis of genes related to antibiotic resistance in a complex microbial community derived from specific samples instead of culture based methods for AMR identification.

Bacterial plasmids are genetic elements that can carry genes for antibiotic resistance from one bacteria to another acting as "messengers". Plasmid transfers contribute to the appearance of multidrug resistant bacteria. This project aims to use a "kill the messenger, not the bacteria" approach to tackle the problem of increasing antibiotic resistance. The goal is to test the elimination of plasmids carrying genes for antimicrobial resistance.

This project will test a sustainable solar oxidation system as a way to remove antibiotic resistant bacteria from wastewater. The hypothesis is that this technology can enable the inactivation of antibiotic resistant bacteria and the elimination of antibiotic resistant genes from effluents in Brazil.

The project aims to monitor AMR in microorganisms of the urinary tract and correlate it with the genetic determinants of resistance in animal enterobacteria. The study results will be disseminated in order to inform potential changes to guidelines regarding selection of the appropriate antimicrobials first-line treatment for urinary tract infections (UTI).

Leonardo MouraUniversidade Federal do Rio de JaneiroRio de Janeiro, Rio de Janeiro, Brazil

Grand Challenges Brazil

Drug Resistance Burden

1 Nov 2018

The project proposes to use an aerobic granular sludge (AGS) - a technology based on microbial community - to remove antibiotics and antimicrobial resistant genes from hospital wastewater. AGS is one of the latest innovations and it has not yet been applied for the treatment of hospital wastewater.

The project will develop a cellulose filter containing immobilized DNA aptamers, molecules that bind to a specific target molecule, that act as specific and high affinity probes for the uptake and retention of antibiotic molecules present in effluents. Nowadays, the removal of antibiotic residues from effluents is mainly based on chemical processes and physical methods that require expensive technologies and costly maintenance. The success of this project will represent a wastewater treatment option that is low-cost and environment-friendly.

Gilberto KacUniversidade Federal do Rio de JaneiroRio de Janeiro, Rio de Janeiro, Brazil

Grand Challenges Brazil

Data Science Approaches

1 Nov 2018

Aims to validate the International Fetal and Newborn Growth Consortium for the 21st century (Intergrowth-21st) standards for gestational weight gain (GWG) and create new recommendations of GWG based on those standards for first trimester normal and overweight women to be used in the Brazilian Unified Health System (SUS). GWG recommendations currently used in SUS have not been properly tested or validated, thus the project might improve prenatal nutritional care and reduce post gestational weight retention.

The main goal of the project is to develop and explore an innovative measure of gestational age - "potential pregnancy days lost" (PPDL) - to produce evidence of its association with maternal and child health, morbidity and mortality in the short, medium and long term. The indicator also aims to convince women and policy makers about the need to promote less interventions and "harm-free care" during pregnancy.

The study aims to develop an Early Childhood Development friendly index (ECD-FI) based on a core set of evidence-based nurturing care indicators to assess the factors contributing to enabling environments and promote ECD at the municipal level by monitoring and identifying opportunities to scale up ECD programs. The index will be created through machine learning and will run analytical models considering demographic information and risk factors at the municipal level. This disaggregated data is not available in Brazil.

Seeks to understand the impacts of the Bolsa Família conditional cash transfer on birth outcomes (e.g., birth weight, gestational weeks, etc). The proposed design will disentangle the measured effects into two components: one that is associated to the cash transfer; and another related to prenatal care assistance. Moreover, this strategy will allow the researchers to determine the window of opportunity where CCT interventions exhibit highest impacts on birth outcomes, recognizing heterogeneous impacts according to how early in the pregnancy the CCT intervention starts.

The project will develop a platform to provide services for decision-making support for neonatal death preventive actions by using data from CIDACS cohort. The platform will offer three services: cohort data visualization for decision-making support by comparative human visual analysis, prediction of risk of neonatal death based on machine learning models, and simulator of public policies impact influencing on the risk of neonatal death.

Aims to access all 68.3 million living births certificates from Brazil, from 1994 to 2016, and compare them with breastfeeding policies in all Brazilian hospitals to assess the impact of the initiatives on infant health. The study also plans to estimate the number of avoidable deaths during this time period, if those initiatives were adopted in Brazil.

By analyzing national children vaccination coverage from spatial perspectives, the study aims to uncover insights into the traditional surveillance. This will help to identify coverage rates, regions of greater vulnerability by providing a differentiated look at the logic of equity in health. Understanding the low childhood vaccination coverage will help to guide public policies for the purpose of interventions.

The study is aimed at evaluating the effectiveness of Mãe Coruja intervention in reducing low birthweight and preterm birth. By using appropriate statistical methods, the study will use the Cidacs dataset combined with the data from Mãe Coruja program to carry out the quasi-experimental study. With the support of machine learning techniques, the project will also Identify social, economic, geographic and environmental conditions that are associated with the outcomes. The researchers will also build an index of perinatal health risk to inform improvements in targeting populations and the deployment of similar strategies and programs elsewhere in Brazil.

Studies show that seasonal influenza in Ceará, in the Northeast region of Brazil, occurs 2 to 3 months earlier than in the South and Southeast, which guides the national calendar of vaccination. By using data science approaches, the study will test if Brazil's current national policy targeting vaccination only during the months of April and May inadequately protects against the harmful maternal-fetal effects of influenza in the Semi-Arid and northern regions of Brazil. If the hypothesis confirms, the study has the potential to change policy and modify the vaccination calendar.

Does air pollution affect the rates of stillbirths, congenital malformations and neonatal mortality? This study aims to answer this question by merging the child health data collected within the 100 Million Brazilian Cohort from Cidacs with high-resolved satellite-derived data on air pollution to establish critical ambient air pollution thresholds for preventing adverse birth outcomes and malformations based on concentrations of fine particles, PM 2.5.

The proposal will develop a platform for the analysis and visualization of data that will allow managers, public servants and other stakeholders involved in the Mãe Coruja Program at Pernambuco state (PMCP) to extract strategic information to improve the intervention. The focus will be on the implementation and actual enforcement of public policies, considering the high gestational risk and sexually transmitted infections (STI). Currently, health databases are for consultations only. The innovation of this proposal is to create an intelligent cloud platform for the analysis and distribution of health information to improve health care of women enrolled in PMCP.

Identifying the preventable causes and performing early risk stratification of pregnant women are instrumental to develop strategies to prevent and reduce preterm birth (PTB). The ability to identify at-risk pregnancies and to enroll women in prevention strategies has been difficult due to complexity of associated risk factors. The study aims to combine different national level data sources to understand the main predictors of PTB and develop a machine-learning-based predictive model to conduct automated risk stratification at the point of care level, integrated with advanced data visualization for clinical decision support.

Infectious diseases may have only transitory impacts on pregnant mothers, but they can have lasting impacts on children. Can public interventions mitigate these impacts? This project aims to identify how exposure to localized epidemiological risk factors in the fetal period influences developmental outcomes for children through the early years of life. The researchers propose to evaluate in what extent the access to primary health care and social welfare programs mitigate negative impacts in child development.

This research aims to analyze the relationship between a conditional cash transfer program and the child's health, considering two generations of the families and using two different approaches: econometric analysis and data mining algorithms. By analyzing the long term impacts of Bolsa Familia program on future generations' health performance, the project will investigate if a child who was born in a family whose grandparents received the cash transfer is in better health conditions than a similar child born in a family whose grandparents did not receive the same benefit.

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